Predictive modeling for adjusting initial values

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predictive modeling for adjusting initial values are disclosed. In one aspect, a method includes the actions of accessing transaction history data that indicates one or more transaction details associated with the transaction, a predicted value, and a final value. The actions further include determining a difference value between the predicted value and the final value. The actions further include generating a predictive model that is trained to estimate. The actions further include receiving one or more transaction details and a predicted value associated with a subsequently received transaction. The actions further include providing the one or more transaction details as input to the predictive model. The actions further include receiving an adjustment value to apply to the predicted value. The actions further include providing, for output, data indicating the adjustment value.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims the benefit of Indian Patent ApplicationNo. 6039/CHE/2015, filed on Nov. 9, 2015, which is incorporated hereinby reference in its entirety for all purposes.

TECHNICAL FIELD

This application generally relates to predictive modeling.

BACKGROUND

Predictive modeling is a process used in predictive analytics to createa statistical model of future behavior. Predictive analytics is the areaof data mining concerned with forecasting probabilities and trends.

SUMMARY

Entities may apply predictive models to future orders to identifyadjustments to initially predicted values. Once an entity has identifieda predicted value for an order, the entity may apply a predictive modelto generate an adjustment value to the predicted value. The predictivemodel is trained to generate an adjustment value that improves theaccuracy of the predicted value so that the adjusted predicted value hasa high likelihood of matching a final value for the order. Thepredictive model may be trained using previous order data, previouspredicted values, and previous final values.

An innovative aspect of the subject matter described in thisspecification may be implemented in a method that includes the actionsof accessing transaction history data that, for each of one or more pasttransactions, indicates one or more transaction details associated withthe transaction, a predicted value, and a final value, wherein the oneor more transaction details include a sentiment associated with anentity requesting the transaction, a value sensitivity of the entity,and a relationship between the entity requesting the transaction and anentity processing the transaction; for each of the one or more pasttransactions, determining a difference value between the predicted valueand the final value; for each of the one or more past transactions,generating, using the one or more transaction details and the differencevalue, a predictive model that is trained to estimate, based on one ormore given transaction details and a given predicted value, anadjustment value to apply to the given predicted value; and receivingone or more transaction details and a predicted value associated with asubsequently received transaction; providing the one or more transactiondetails as input to the predictive model; in response to providing theone or more transaction details as input to the predictive model,receiving, from the predictive model, an adjustment value to apply tothe predicted value; and providing, for output, data indicating theadjustment value.

These and other implementations can each optionally include one or moreof the following features. The actions include accessing real-time datathat is associated with the entity requesting the transaction; and basedon the real-time data that is associated with the entity requesting thetransaction, determining a real-time sentiment that is associated withthe entity requesting the transaction. The action of providing the oneor more transaction details as input to the predictive model includesproviding data indicating the real-time sentiment that is associatedwith the entity requesting the transaction as additional input to thepredictive model. The actions include accessing real-time data that isassociated with the entity requesting the transaction and with theentity processing the transaction; and based on the real-time data thatis associated with the entity requesting the transaction, determining arelationship between the entity requesting the transaction and theentity processing the transaction.

The action of providing the one or more transaction details as input tothe predictive model includes providing data indicating the relationshipbetween the entity requesting the transaction and the entity processingthe transaction as additional input to the predictive model. Thesentiment associated with an entity requesting the transaction is basedon an analysis of media that includes references to the entityrequesting the transaction and contexts of the references. The valuesensitivity of the entity requesting the transaction is based on alikelihood of the entity to request adjusting the predicted value. Thetransaction is a stock trade. The predicted value is a predicted priceper share. The adjustment value is an adjustment to the predicted priceper share. The relationship between the entity requesting thetransaction and the entity processing the transaction is based on aprofitability realized by the entity processing the transaction inproviding services to the entity requesting the transaction. The actionsinclude receiving data indicating an instruction to apply the adjustmentvalue to the predicted value; and executing the transaction with thepredicted value adjusted by the adjustment value. The transactiondetails include compliance characteristics of the entity requesting thetransaction based on a number of changes the entity requesting thetransaction has requested for previous transactions.

Other implementations of this aspect include corresponding systems,apparatus, and computer programs recorded on computer storage devices,each configured to perform the operations of the methods.

Another innovative aspect of the subject matter described in thisspecification may be implemented in a method that includes the actionsof accessing transaction history data that, for each of one or more pasttransactions, indicates one or more transaction details associated withthe transaction, a predicted value, and a final value, wherein the oneor more transaction details include a sentiment associated with anentity requesting the transaction, a value sensitivity of the entity,and a relationship between the entity requesting the transaction and anentity processing the transaction; for each of the one or more pasttransactions, determining a difference value between the predicted valueand the final value; for each of the one or more past transactions,generating, using the one or more transaction details and the differencevalue, a predictive model that is trained to estimate, based on one ormore given transaction details and a given predicted value, anadjustment value to apply to the given predicted value; and using thepredictive model to estimate an adjustment value to apply to one or moresubsequently received transaction details associated with a subsequenttransaction.

These and other implementations can each optionally include one or moreof the following features. The sentiment associated with an entityrequesting the transaction is based on an analysis of media thatincludes references to the entity requesting the transaction andcontexts of the references. The value sensitivity of the entityrequesting the transaction is based on a likelihood of the entity torequest adjusting the predicted value. The transaction is a stock trade.The predicted value is a predicted price per share. The adjustment valueis an adjustment to the predicted price per share. The relationshipbetween the entity requesting the transaction and the entity processingthe transaction is based on a profitability realized by the entityprocessing the transaction in providing services to the entityrequesting the transaction. The transaction details include compliancecharacteristics of the entity requesting the transaction based on anumber of changes the entity requesting the transaction has requestedfor previous transactions.

Other implementations of this aspect include corresponding systems,apparatus, and computer programs recorded on computer storage devices,each configured to perform the operations of the methods.

Another innovative aspect of the subject matter described in thisspecification may be implemented in a method that includes the actionsof accessing a predictive model that is trained to estimate, based onone or more given transaction details and a given predicted value, anadjustment value to apply to the given predicted value, wherein the oneor more transaction details include a sentiment associated with anentity requesting the transaction, a value sensitivity of the entity,and a relationship between the entity requesting the transaction and anentity processing the transaction; receiving one or more transactiondetails and a predicted value associated with a subsequently receivedtransaction; providing the one or more transaction details as input tothe predictive model; in response to providing the one or moretransaction details as input to the predictive model, receiving, fromthe predictive model, an adjustment value to apply to the predictedvalue; and providing, for output, data indicating the adjustment value.

These and other implementations can each optionally include one or moreof the following features. The actions include accessing real-time datathat is associated with the entity requesting the transaction; and basedon the real-time data that is associated with the entity requesting thetransaction, determining a real-time sentiment that is associated withthe entity requesting the transaction. The action of providing the oneor more transaction details as input to the predictive model includesproviding data indicating the real-time sentiment that is associatedwith the entity requesting the transaction as additional input to thepredictive model. The actions include accessing real-time data that isassociated with the entity requesting the transaction and with theentity processing the transaction; and based on the real-time data thatis associated with the entity requesting the transaction, determining arelationship between the entity requesting the transaction and theentity processing the transaction. The action of providing the one ormore transaction details as input to the predictive model includesproviding data indicating the relationship between the entity requestingthe transaction and the entity processing the transaction as additionalinput to the predictive model. The actions include receiving dataindicating an instruction to apply the adjustment value to the predictedvalue; and executing the transaction with the predicted value adjustedby the adjustment value. The transaction details include compliancecharacteristics of the entity requesting the transaction based on anumber of changes the entity requesting the transaction has requestedfor previous transactions.

Other implementations of this aspect include corresponding systems,apparatus, and computer programs recorded on computer storage devices,each configured to perform the operations of the methods.

Particular implementations of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages. A system may identify a value for an order thathas the highest likelihood of providing the most benefit to the entityfacilitating the order.

The details of one or more implementations of the subject matterdescribed in this specification are set forth in the accompanyingdrawings and the description below. Other features, aspects, andadvantages of the subject matter will become apparent from thedescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system for predicting values fortransactions.

FIG. 2 illustrates an example user interface for predicting values fortransactions.

FIG. 3 illustrates an example process for predicting values fortransactions.

FIG. 4 illustrates an example of a computing device and a mobilecomputing device.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIG. 1 illustrates an example system 100 for predicting values fortransactions. Briefly, and as described in further detail below, thesystem 100 assists a user in estimating an adjustment value to apply toa predicted value for a transaction. The system 100 may generate theadjustment value based on an output of a predictive model that processesdata related to the transaction such as sentiment data, data related toan entity associated with the transaction (e.g., news events), andprevious transactions.

The machine learning system 105 generates predictive models that arestored in the predictive model storage 110 and used by the transactionexecution processor 115 during the processing of a transaction that isrequested by an entity. Each predictive model is configured to providean adjustment value to apply to a predicted value for a giventransaction in addition to a confidence value for the adjustment value.For example, machine learning system 105 may use supervised learningtechniques such as neural networks to generate the predictive models.The machine learning system 105 may generate a predictive model for eachentity that requests transactions, for a group of entities or for allentities.

The machine learning system 105 accesses data stored on the microblogserver 120. The microblog server 120 provides a platform for users towrite and share microblogs. The microblog server 120 may provide anapplication programming interface (API) so that other systems can accessand retrieve the microblog data directly over the internet 122. Themicroblogs written by the users may be related to any topic. Forexample, one user may write about a positive experience that the userhad interacting with an entity. Another user may write about a negativeexperience that the user has interacting with the same entity. Manyother users may write about experiences with the same entity. Themachine learning system 105 may retrieve the microblogs related to oneparticular entity and extract a sentiment that users may be feelingtowards that the entity. A positive sentiment suggests than values tiedto an entity should be increased. A negative sentiment suggests thatvalues tied to an entity should be decreased. The microblogs may betimestamped so that the machine learning system 105 may correlate a timeperiod with each sentiment. In some implementations, the machine leaningsystem 105 receives sentiments and associated timestamps from a servicethat processes the microblogs to generate sentiments.

The machine learning system 105 accesses data stored on the news server125. The news server 125 aggregates news articles written by variousnews sources and provides an API for other system to access and retrievethe articles directly over the internet 122. In some implementations,the news server 125 is a collection of different news servers thatprovide articles for users to read over the internet. In this instance,the machine learning system 105 may access each news server, possiblythrough an API, to retrieve articles. Similar to the microblogs, themachine learning system 105 may analyze the articles to identify thearticles that mention different entities. The machine learning system105 may analyzes the articles that mention a particular entity todetermine a sentiment associated with that particular entity. A positivenews outlook suggests than values tied to an entity should be increased.A negative news outlook suggests that values tied to an entity should bedecreased. The machine learning system 105 may also attach a timestampto the sentiment based on when the news server 125 published thearticles. In addition to sentiments based on the news articles, themachine learning system 105 may identify particular events related toeach entity based on the articles. For example, the machine learningsystem 105 may identify articles that are related to a particular entityhiring a new chief executive or the entity planning on reducing the sizeof its workforce. As another example, the machine learning system 105may identify articles related to a particular entity that describes anupcoming earnings report or major recall. In some implementations, themachine learning system receives sentiments, events, and associatedtimestamps from a service that processes the news articles.

The machine learning system 105 accesses the transaction history data130. The transaction history data 130 stores the data related to each ofprevious transactions. In particular the transaction history data 130stores data for the entity requesting the transaction, the goods boughtor sold in the transaction, the predicted value for the transaction, thefinal value for the transaction, and a timestamp. As an example, atransaction may involve ABC Fund selling five hundred thousand shares ofstock. The stock trader accesses his trading system that suggests aprice of fifty dollars per share. The stock trader offers ABC fund aprice of 49.92 dollars per share. Once the stock trader sells all theshares of the stock, the final average price is 49.91. The difference inprice suggests that the stock trader lost some money on the transactionby offering too high of a price per share. The transaction history data130 may also store data related to liquidity for different goods orsecurities. Typically, a good that is less liquid is more difficult tosell and may cause the price to be less predictable. In someimplementations, the transaction history data 130 offers an API that themachine learning system 105 uses to access the data.

The transaction history data 130 may also store data related any traderestrictions that were present during a transaction. For example, anentity requesting the transaction, or client, may have recently enteredan agreement with the entity performing the transaction to beginperforming transactions. The agreement may be for a particular type ofgood or security. The client may be restricted from trading other typesof goods or securities. As another example, the client may lacesufficient margin on the client's account for the size of the requestedtransaction. The transaction history data 130 may also include thebid-ask spread for a particular transaction as well as the availabilityof any dark pool trading options for a particular security.

The machine learning system 105 access relationship history data 135.The relationship history data 135 includes data relation to the behaviorof each entity during previous transactions. The data includesprofitability data for each entity. The profitability indicates anamount of profit that the entity operating the system 100 has earnedfrom doing business with each entity. The profitability data mayindicate an amount of profit that the entity operating the system 100has earned from each transaction with each entity. For example, theprofitability data may indicate that the entity operating the system 100has earned an average of ten thousand dollars from each transaction withABC Fund. The machine learning system 105 accesses the profitabilitydata and analyses the data for trends. For example, the profitabilitydata for ABC Fund may indicate that the profitability for eachtransaction is has been steadily decreasing.

As another example, the relationship history data 135 may be related toa cost to serve for a particular entity, or client. Clients may beresponsible for providing Standard Settlement Instructions and otherdetails required to confirm a transaction. Some clients may not providethis information in a timely manner or provide incorrect information.Clients may also request special processing of a transaction such asrequesting confirmation statements in paper form or in multiplelanguages or both. These actions cause the entity performing thetransaction to perform extra work to identify the correct informationand rectify the problem.

The relationship history data 135 includes compliance characteristicsfor each entity. The compliance characteristics indicate an amount ofservice that each entity requested during each prior transaction. Theservice may be related to the number of questions or requests that theentity had related to a particular transaction. The machine learningsystem 105 may receive the compliance characteristics compare thequestions and requests for each of the entity's transactions. Themachine learning system 105 may generate a quantity that represents anattention level that a client typically requires, where an entity withhigh level of attention utilizes more resources of the entity operatingthe system 100 than an entity with a low level of attention. The machinelearning system 105 may identify trends in the level of attentionrequired by each entity. For example, the level of attention require byABC Fund may be decreasing during the recent period.

The relationship history data 135 includes value sensitivity data foreach entity. The value sensitivity data may indicate an entity'shistorical behavior with respect to pushing for price adjustments. Forexample, a stock trader stock trader offers ABC fund a price of 49.92dollars per share for stock that ABC Fund is selling. ABC Fund may tryto negotiate a price of 49.88 dollars per share. In response to thenegotiation, the stock trader may offer ABC Fund a price of 49.91dollars per share. For this transaction, the value sensitivity data mayindicate that ABC Fund attempted to adjust the price by 0.04 dollar pershare and the stock trader adjusted the price 0.01 dollars per share.The value sensitivity data may also include percentage related to therequested adjustment and the actual adjustment.

The machine learning system 105 generates predictive models based on thedata received from the microblog server 120, the news server 125, thetransaction history data 130, and the relationship history server 135.The machine learning system 105 may generate predictive models usingneural networks with a specified number of hidden layers received fromthe entity operating the system 100. The machine learning system 105 maycontinue to adjust the predictive models as additional transactions anddata are received from the microblog server 120, the news server 125,the transaction history data 130, and the relationship history server135. The adjustments may occur on a periodic basis such as every day orin real-time as the machine learning system 105 receives the data. Thepredictive models that may or may not be entity specific are stored inthe predictive model storage 110. In some implementations, the entityperforming the transaction may adjust the predictive models based on anamount of risk with which the entity is comfortable. The adjustmentsbased on risk may occur daily or more than once per day and may bedriven by the entity's views of market events, macroeconomic outlook,research, or other similar reasons. By adjusting the risk, the entitymay maintain or increase profitability by taking on more risk infavorable conditions and taking on less risk in less favorableconditions.

The transaction execution processor 115 receives a request for atransaction from the entity operating the system 100. As the transactionexecution processor 115 receives a request for a transaction from theentity operating the system 100, or the entity performing thetransaction, the legacy valuation processor 117 receives a similarrequest for the transaction. The legacy valuation processor 117 applieslegacy liquidity and pricing algorithms to calculate a value for arequested good. The legacy liquidity and pricing algorithms may factorin the number of pending transactions for the good. A good with manypurchase orders compared to sell orders may be difficult to buy, and agood with few purchase orders compared to sell orders may be difficultto sell. The value calculated by the legacy valuation processor 117 maybe used by the entity operating the system 100, but the value may not beproperly adjusted to take into account current events, currentsentiment, a current relationship between the entity and the client, andprevious transactions between the entity and the client. To adjust thevalue, the transaction execution processor 115 applies a predictivemodel stored in the predictive model storage 110. The predictive modelcalculates an adjustment to the calculated price based on data similarto the data used to train the predictive model.

The transaction execution processor 115 receives data from the microblogserver 120 to determine the current sentiment. The transaction executionprocessor 115 receives data from the news server 125 to determinecurrent events. The transaction execution processor 115 receivestransaction history data 130 that may include data related to thecurrent value sensitivity of the entity and any value sensitivitytrends. The transaction execution processor 115 receives relationshipdata for the entity from the relationship history data 135. Using thisdata, the transaction execution processor 115 calculates an adjustmentvalue to apply to the suggested value. The user operating the system 100may choose to apply the suggested value and provide the entityrequesting the transaction with the value adjusted value. In someimplementations, the predictive models may be entity specific. In thisinstance, the transaction execution processor 115 may only input thedata from the microblog server 120 and the news server 125 as well asthe goods and quantity to buy or sell into the predictive model.

The transaction execution processor 115 provides the output of thepredictive model to the display 140. The display 140 provides avisualization of data from the microblog server 120, the news server125, the transaction history data 130, and the relationship historyserver 135. Display corner 145 includes data related to the currenttransaction. The current transaction is the one requested by the entitythat communicated the transaction to the entity operating the system100. In this example, the entity request to buy ten thousand shares ofXYZ stock. The suggested value as calculated by the transactionexecution processor 115 pricing and liquidity algorithms is 63.7 dollarsper share. The transaction execution processor 115 uses the predictivemodel to generate an adjustment value of decreasing the price 0.2dollars per share. The transaction execution processor 115 also providesa confidence value for the adjustment value, which in this example, isninety-two percent. The confidence value may be based in part on anamount of training data that the machine learning system used togenerate the predictive model.

An example use case for the system 100 may be related to adjustingratings for restaurants where a magazine has given a rating to therestaurant. The rating provided by the magazine may be similar to avalue provided by a legacy valuation system 117. The transactionexecution processor 115 applies a predictive model and additionalsignals to adjust the rating provided by the magazine to improve itsaccuracy. The machine learning system 105 may generate a predictivemodel based on previous ratings given by customers and any currentevents or sentiments that were present when each customer rated therestaurant. The transaction execution processor 115 may receive themagazine rating and the current news events and any sentiments thatusers may be posting online and apply the predictive model. The adjustedrating factors in the current sentiments and events but may not be asvolatile as a current average user rating.

Another example use case for the system 100 may be related to adjustingprices for stocks as a trader receives a large order from a client. Thetrader's legacy valuation processor 117 may take into account thecurrent state of the market such as current requests for purchases andsales and the current bid-offer spread. The trader is responsible forproviding the client with the most accurate price and the price from thelegacy system may not factor in the current sentiment and news. In someimplementations, the trader underwrites the transaction. The transactionexecution processor 115 receives a predictive model and currentsentiment data, current news data, updated transaction history data, andcurrent relationship data for the client. The transaction executionprocessor 115 generates a price adjustment to apply to the priceprovided by the legacy system. The trader receives the price adjustmentthrough a graphical user interface. The trader may verify the priceadjustment by analyzing the current sentiment and news manually andprovide the adjusted price to the client.

The goal of the system 100 is to provide the client with an accurateprice quote. Because the client may be purchasing or selling many sharesof a particular security, the trader offers the client a price and thenthe trader attempts to execute the trade on the market. A more accurateprice provides improved certainty for the trader and the client becausewith large orders even small price changes have large consequences forthe trader and the client.

FIG. 2 illustrates an example user interface 200 for predicting valuesfor transactions. The user interface 200 illustrates a more detailedview of the display corner 145 from FIG. 1. The user interface 200provides to the user a visualization of the transaction, the entityrequesting the transaction, the goods, the value, current news andevents, and an output of the predictive model.

The user interface 200 includes a client information section 210. In theclient information section the user inputs the name of the client, thegoods that the client is buying or selling, and the quantity of goods.In this example, ABC company is placing an order for one hundredthousand of XYZ goods. The user interface 200 includes a recommendationsection 220. The recommendation section 220 includes information relatedto the current values for the goods. In this example, the XYZ goods areavailable for purchase at 23.1 dollars each and can be sold at 22.23dollars each. The recommendation section notes the various venues thatthe XYZ goods may be bought and sold. The system may identify thevarious venues based on which venues have an approximately equal numberof purchases and sales of the XYZ goods. The recommendation section 200lists the main venue where the XYZ goods will likely be bought and soldas well as alternative venues.

The user interface 200 includes a news flow summary section 230. Thenews flow summary section 230 provides the user with information relatedto the current events and a sentiment related to the entity. The currentevents indicator displays an indication of a likelihood of an eventoccurring that affects the ability of an entity to complete thetransaction at the price provided by the user. In this example, the newsflow summary section 230 indicates that there is a high likelihood of anevent occurring that may impact the closing of the transaction. The newsflow summary section 230 also indicates that the current sentiment ofthe entity is positive. The news flow summary section 230 also indicatesa sample headline of the articles processed by the system. The news flowsummary section 230 allows the user to click any of the informationsections to read more detail.

The user interface 200 includes a pricing factors section 240. Thepricing factors section 240 includes details related to the entityrequesting the transaction. The details may be related to therelationship between the entity and the user's company. For example, thepricing factors section 240 may indication a relationship level betweenthe entity and the user's company. The relationship level may be basedon a profitability for user's company by providing services to theentity relative to other companies. In user interface 200, the entity isa priority client for the user's company suggesting that serving theentity is very profitable. The pricing factors section 240 includes anindication of how much interaction was involved in previously servingthe entity. For example, serving the entity may have required an averagelevel of interaction such as answering a few inquiries and otherquestions. This history may place the entity in the neutral group.

The user interface 200 includes a visualization of the number ofpre-transaction checks 250 that the system has performed. Thepre-transaction checks 250 indicate whether the system has verified thetransaction limits. The transaction limits may specify the amount ofgoods that an entity or the user's company may be able to buy or sell ina given period. For example, the user's company may be restricted tobeing involved in buying and selling over one million units of aparticular good in a twenty-four hour period. The pre-transaction checks250 indicate whether the system has verified the compliance checks forthe entity and the user's company. In some instances, there may berestrictions on buying or selling a particular good either by the entityor the user's company. For example, the entity may be prohibited frombuying good DEF.

The pre-transaction checks 250 indicate whether the system has verifiedthe transaction instructions. For this check, the system may verify thatthe transaction instructions are complete that no order details aremissing. The pre-transaction checks 250 indicate whether the system hasverified that the order is within any required thresholds. For example,the entity may not be authorized to buy or sell more than one millionunits of a particular good in one transaction. The pre-transactionchecks 250 indicate whether the system has verified whether there areany restrictions on the entity or the user's company for buying orselling the goods. For example, the user's company may have arestriction on selling or buying a particular good for the next twohours.

The pre-transaction checks 250 indicate research/transaction ideas 260to offer the user. The research/transaction ideas 260 indicate the valueadjustments that the system recommends to the user based on applying theorder details, news flow, pricing factors, and the pre-transactionchecks to the predictive model. The research/transaction ideas 260 mayinclude more than one value adjustment. For example, theresearch/transaction ideas 260 may provide transaction strategy ABC.Selecting strategy ABC may display an adjustment of a 0.03 increase inthe value with an eighty-five percent confidence. Selecting strategy DEFmay display an adjustment of a 0.04 increase with a seventy percentconfidence. Each of the strategies displayed in the research/transactionideas 260 may be generated with different predictive models. The usermay consider the provided strategies and provide a value to offer to theentity to execute the transaction.

FIG. 3 is a flow chart of an example process 300 for predicting valuesfor transactions. In general, the process 300 provides an adjustmentvalue for a user to apply to a predicted value to maximize profitabilityof a transaction. The process 300 will be described as being performedby a computer system comprising one or more computers, for example, thesystem 100 as shown in FIG. 1.

The system accesses transaction history data that, for each of one ormore past transactions, indicates one or more transaction detailsassociated with the transaction, a predicted value, and a final value,where the one or more transaction details include a sentiment associatedwith an entity requesting the transaction, a value sensitivity of theentity, and a relationship between the entity requesting the transactionand an entity processing the transaction (310). In some implementations,the sentiment associated with an entity requesting the transaction isbased on an analysis of media that includes references to the entityrequesting the transaction and contexts of the references. For example,the sentiment may be obtained from microblogging websites where usersdiscuss and post about various topics including the entity requestingthe transaction. In some implementations, the value sensitivity of theentity requesting the transaction is based on a likelihood of the entityto request adjusting the predicted value. For example, the entityrequesting the transaction may typically push for a higher value whenselling a good or a lower value when purchasing the good.

In some implementations, the relationship between the entity requestingthe transaction and the entity processing the transaction is based on aprofitability realized by the entity processing the transaction inproviding services to the entity requesting the transaction. Forexample, providing transaction services to the entity requesting thetransaction generate higher profits compared to other entities thatrequest transactions. In some implementations, the transaction detailsinclude compliance characteristics of the entity requesting thetransaction based on a number of changes the entity requesting thetransaction has requested for previous transactions. For example, theentity requesting the transaction may require a high level of servicebecause the entity requires confirmation in multiple languages and paperconfirmations, thus increasing costs to the entity processing thetransaction. In some implementations, the transaction is a stock trade,the predicted value is a predicted price per share, and the adjustmentvalue is an adjustment to the predicted price per share.

The system, for each of the one or more past transactions, determines adifference value between the predicted value and the final value (320).The system, for each of the one or more past transactions, generates,using the one or more transaction details and the difference value, apredictive model that is trained to estimate, based on one or more giventransaction details and a given predicted value, an adjustment value toapply to the given predicted value (330).

The system receives one or more transaction details and a predictedvalue associated with a subsequently received transaction (340). In someimplementations, the system accesses real-time data that is associatedwith the entity requesting the transaction and based on the real-timedata that is associated with the entity requesting the transaction,determine a real-time sentiment that is associated with the entityrequesting the transaction. In some implementations, the systemproviding data indicating the real-time sentiment that is associatedwith the entity requesting the transaction as additional input to thepredictive model. For example, the system may retrieve real-time datafrom microblogging servers for generating a real-time sentiment to applyto the predictive model.

In some implementations, the system accesses real-time data that isassociated with the entity requesting the transaction and with theentity processing the transaction. In some implementations, based on thereal-time data that is associated with the entity requesting thetransaction, the system determines a relationship between the entityrequesting the transaction and the entity processing the transaction.The system provides data indicating the relationship between the entityrequesting the transaction and the entity processing the transaction asadditional input to the predictive model. For example, the system mayretrieve real-time data from new servers for generating a real-time newsoutlook to apply to the predictive model.

The system provides the one or more transaction details as input to thepredictive model (350). The system, in response to providing the one ormore transaction details as input to the predictive model, receives,from the predictive model, an adjustment value to apply to the predictedvalue (360). The system provides, for output, data indicating theadjustment value (370). In some implementations, the system receivesdata indicating an instruction to apply the adjustment value to thepredicted value and executes the transaction with the predicted valueadjusted by the adjustment value.

FIG. 4 shows an example of a computing device 400 and a mobile computingdevice 450 that can be used to implement the techniques described here.The computing device 400 is intended to represent various forms ofdigital computers, such as laptops, desktops, workstations, personaldigital assistants, servers, blade servers, mainframes, and otherappropriate computers. The mobile computing device 450 is intended torepresent various forms of mobile devices, such as personal digitalassistants, cellular telephones, smart-phones, and other similarcomputing devices. The components shown here, their connections andrelationships, and their functions, are meant to be examples only, andare not meant to be limiting.

The computing device 400 includes a processor 402, a memory 404, astorage device 406, a high-speed interface 408 connecting to the memory404 and multiple high-speed expansion ports 410, and a low-speedinterface 412 connecting to a low-speed expansion port 414 and thestorage device 406. Each of the processor 402, the memory 404, thestorage device 406, the high-speed interface 408, the high-speedexpansion ports 410, and the low-speed interface 412, are interconnectedusing various busses, and may be mounted on a common motherboard or inother manners as appropriate. The processor 402 can process instructionsfor execution within the computing device 400, including instructionsstored in the memory 404 or on the storage device 406 to displaygraphical information for a GUI on an external input/output device, suchas a display 416 coupled to the high-speed interface 408. In otherimplementations, multiple processors and/or multiple buses may be used,as appropriate, along with multiple memories and types of memory. Also,multiple computing devices may be connected, with each device providingportions of the necessary operations (e.g., as a server bank, a group ofblade servers, or a multi-processor system).

The memory 404 stores information within the computing device 400. Insome implementations, the memory 404 is a volatile memory unit or units.In some implementations, the memory 404 is a non-volatile memory unit orunits. The memory 404 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 406 is capable of providing mass storage for thecomputing device 400. In some implementations, the storage device 406may be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. Instructions can be stored in an information carrier.The instructions, when executed by one or more processing devices (forexample, processor 402), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices such as computer- or machine-readable mediums (forexample, the memory 404, the storage device 406, or memory on theprocessor 402).

The high-speed interface 408 manages bandwidth-intensive operations forthe computing device 400, while the low-speed interface 412 manageslower bandwidth-intensive operations. Such allocation of functions is anexample only. In some implementations, the high-speed interface 408 iscoupled to the memory 404, the display 416 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 410,which may accept various expansion cards (not shown). In theimplementation, the low-speed interface 412 is coupled to the storagedevice 406 and the low-speed expansion port 414. The low-speed expansionport 414, which may include various communication ports (e.g., USB,Bluetooth, Ethernet, wireless Ethernet) may be coupled to one or moreinput/output devices, such as a keyboard, a pointing device, a scanner,or a networking device such as a switch or router, e.g., through anetwork adapter.

The computing device 400 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 420, or multiple times in a group of such servers. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 422. It may also be implemented as part of a rack server system424. Alternatively, components from the computing device 400 may becombined with other components in a mobile device (not shown), such as amobile computing device 450. Each of such devices may contain one ormore of the computing device 400 and the mobile computing device 450,and an entire system may be made up of multiple computing devicescommunicating with each other.

The mobile computing device 450 includes a processor 452, a memory 464,an input/output device such as a display 454, a communication interface466, and a transceiver 468, among other components. The mobile computingdevice 450 may also be provided with a storage device, such as amicro-drive or other device, to provide additional storage. Each of theprocessor 452, the memory 464, the display 454, the communicationinterface 466, and the transceiver 468, are interconnected using variousbuses, and several of the components may be mounted on a commonmotherboard or in other manners as appropriate.

The processor 452 can execute instructions within the mobile computingdevice 450, including instructions stored in the memory 464. Theprocessor 452 may be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 452may provide, for example, for coordination of the other components ofthe mobile computing device 450, such as control of user interfaces,applications run by the mobile computing device 450, and wirelesscommunication by the mobile computing device 450.

The processor 452 may communicate with a user through a controlinterface 458 and a display interface 456 coupled to the display 454.The display 454 may be, for example, a TFT (Thin-Film-Transistor LiquidCrystal Display) display or an OLED (Organic Light Emitting Diode)display, or other appropriate display technology. The display interface456 may comprise appropriate circuitry for driving the display 454 topresent graphical and other information to a user. The control interface458 may receive commands from a user and convert them for submission tothe processor 452. In addition, an external interface 462 may providecommunication with the processor 452, so as to enable near areacommunication of the mobile computing device 450 with other devices. Theexternal interface 462 may provide, for example, for wired communicationin some implementations, or for wireless communication in otherimplementations, and multiple interfaces may also be used.

The memory 464 stores information within the mobile computing device450. The memory 464 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 474 may also beprovided and connected to the mobile computing device 450 through anexpansion interface 472, which may include, for example, a SIMM (SingleIn Line Memory Module) card interface. The expansion memory 474 mayprovide extra storage space for the mobile computing device 450, or mayalso store applications or other information for the mobile computingdevice 450. Specifically, the expansion memory 474 may includeinstructions to carry out or supplement the processes described above,and may include secure information also. Thus, for example, theexpansion memory 474 may be provide as a security module for the mobilecomputing device 450, and may be programmed with instructions thatpermit secure use of the mobile computing device 450. In addition,secure applications may be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory(non-volatile random access memory), as discussed below. In someimplementations, instructions are stored in an information carrier. thatthe instructions, when executed by one or more processing devices (forexample, processor 452), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices, such as one or more computer- or machine-readablemediums (for example, the memory 464, the expansion memory 474, ormemory on the processor 452). In some implementations, the instructionscan be received in a propagated signal, for example, over thetransceiver 468 or the external interface 462.

The mobile computing device 450 may communicate wirelessly through thecommunication interface 466, which may include digital signal processingcircuitry where necessary. The communication interface 466 may providefor communications under various modes or protocols, such as GSM voicecalls (Global System for Mobile communications), SMS (Short MessageService), EMS (Enhanced Messaging Service), or MMS messaging (MultimediaMessaging Service), CDMA (code division multiple access), TDMA (timedivision multiple access), PDC (Personal Digital Cellular), WCDMA(Wideband Code Division Multiple Access), CDMA2000, or GPRS (GeneralPacket Radio Service), among others. Such communication may occur, forexample, through the transceiver 468 using a radio-frequency. Inaddition, short-range communication may occur, such as using aBluetooth, WiFi, or other such transceiver (not shown). In addition, aGPS (Global Positioning System) receiver module 470 may provideadditional navigation- and location-related wireless data to the mobilecomputing device 450, which may be used as appropriate by applicationsrunning on the mobile computing device 450.

The mobile computing device 450 may also communicate audibly using anaudio codec 460, which may receive spoken information from a user andconvert it to usable digital information. The audio codec 460 maylikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 450. Such sound mayinclude sound from voice telephone calls, may include recorded sound(e.g., voice messages, music files, etc.) and may also include soundgenerated by applications operating on the mobile computing device 450.

The mobile computing device 450 may be implemented in a number ofdifferent forms, as shown in the figure. For example, it may beimplemented as a cellular telephone 480. It may also be implemented aspart of a smart-phone 582, personal digital assistant, or other similarmobile device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms machine-readable medium andcomputer-readable medium refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term machine-readable signal refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (LAN), a wide area network (WAN), and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Although a few implementations have been described in detail above,other modifications are possible. For example, while a clientapplication is described as accessing the delegate(s), in otherimplementations the delegate(s) may be employed by other applicationsimplemented by one or more processors, such as an application executingon one or more servers. In addition, the logic flows depicted in thefigures do not require the particular order shown, or sequential order,to achieve desirable results. In addition, other actions may beprovided, or actions may be eliminated, from the described flows, andother components may be added to, or removed from, the describedsystems. Accordingly, other implementations are within the scope of thefollowing claims.

What is claimed is:
 1. A computer-implemented method comprising:accessing transaction history data that, for each of one or more pasttransactions, indicates past transaction details associated with thepast transaction, a past predicted value, and a past final value,wherein the past transaction details include a past sentiment associatedwith a past requesting entity requesting the past transaction, a pastvalue sensitivity of the past requesting entity, and a past relationshipbetween the past requesting entity requesting the past transaction and apast processing entity processing the past transaction; for each of theone or more past transactions, determining a past difference valuebetween the past predicted value and the past final value; training,using machine learning and the past transaction details and the pastdifference value, a predictive model that is configured to receive agiven sentiment associated with a given requesting entity requesting agiven transaction, a given value sensitivity of the given requestingentity, a given relationship between the given requesting entity and agiven processing entity processing the given transaction, and a givenpredicted value associated with the given transaction and output a givendifference value to apply to the given predicted value; receiving asentiment associated with a requesting entity requesting a transaction,a value sensitivity of the requesting entity, a relationship between therequesting entity and a processing entity processing the transaction,and a predicted value associated with the transaction; providing thesentiment associated with the requesting entity requesting thetransaction, the value sensitivity of the requesting entity, therelationship between the requesting entity and the processing entityprocessing the transaction, and the predicted value associated with thetransaction as input to the predictive model; in response to providingthe sentiment associated with the requesting entity requesting thetransaction, the value sensitivity of the requesting entity, therelationship between the requesting entity and the processing entityprocessing the transaction, and the predicted value associated with thetransaction as input to the predictive model, receiving, from thepredictive model, an adjustment value to apply to the predicted value;and providing, for output, data indicating the adjustment value.
 2. Themethod of claim 1, wherein: the past sentiment associated with the pastrequesting entity is a past real-time sentiment that is based on pastreal-time data associated with the past requesting entity, the givensentiment associated with the given requesting entity is a givenreal-time sentiment that is based on given real-time data associatedwith the given requesting entity, and the sentiment associated with therequesting entity is a real-time sentiment that is based on real-timedata associated with the requesting entity.
 3. The method of claim 1,wherein: the past relationship between the past requesting entityrequesting the past transaction and the past processing entityprocessing the past transaction is based on past real-time dataassociated with the past requesting entity and the past processingentity, the given relationship between the given requesting entityrequesting the given transaction and the given processing entityprocessing the given transaction is based on given real-time dataassociated with the given requesting entity and the given processingentity, and the relationship between the requesting entity requestingthe transaction and the processing entity processing the transaction isbased on real-time data associated with the requesting entity and theprocessing entity.
 4. The method of claim 1, wherein: the past sentimentassociated with the past requesting entity requesting the pasttransaction is based on an analysis of past media that includes pastreferences to the past requesting entity requesting the past transactionand contexts of the past references, the given sentiment associated withthe given requesting entity requesting the given transaction is based onan analysis of given media that includes given references to the givenrequesting entity requesting the given transaction and contexts of thegiven references, and the sentiment associated with the requestingentity requesting the transaction is based on an analysis of media thatincludes references to the requesting entity requesting the transactionand contexts of the references.
 5. The method of claim 1, wherein: thepast value sensitivity of the past requesting entity requesting the pasttransaction is based on a past likelihood of the past requesting entityrequesting an adjustment to the past predicted value, the given valuesensitivity of the given requesting entity requesting the giventransaction is based on a given likelihood of the given requestingentity requesting an adjustment to the given predicted value, and thevalue sensitivity of the requesting entity requesting the transaction isbased on a likelihood of the requesting entity requesting an adjustmentto the predicted value.
 6. The method of claim 1, wherein: thetransaction is a stock trade, the predicted value is a predicted priceper share, and the adjustment value is an adjustment to the predictedprice per share.
 7. The method of claim 1, wherein: the pastrelationship between the past requesting entity requesting the pasttransaction and the past processing entity processing the pasttransaction is based on a past profitability realized by the pastprocessing entity processing the past transaction in providing pastservices to the past requesting entity requesting the past transaction,the given relationship between the given requesting entity requestingthe given transaction and the given processing entity processing thegiven transaction is based on a given profitability realized by thegiven processing entity processing the given transaction in providinggiven services to the given requesting entity requesting the giventransaction, and the relationship between the requesting entityrequesting the transaction and the processing entity processing thetransaction is based on a profitability realized by the processingentity processing the transaction in providing services to therequesting entity requesting the transaction.
 8. The method of claim 1,comprising: receiving data indicating an instruction to apply theadjustment value to the predicted value; and executing the transactionwith the predicted value adjusted by the adjustment value.
 9. The methodof claim 1, wherein: the past relationship between the past requestingentity requesting the past transaction and the past processing entityprocessing the past transaction is based on a number of changes the pastrequesting entity requesting the past transaction has requested for pastprevious transactions with the past processing entity, the givenrelationship between the given requesting entity requesting the giventransaction and the given processing entity processing the giventransaction is based on a number of changes the given requesting entityrequesting the given transaction has requested for given previoustransactions with the given processing entity, and the relationshipbetween the requesting entity requesting the transaction and theprocessing entity processing the transaction is based on a number ofchanges the requesting entity requesting the transaction has requestedfor previous transactions with the processing entity.
 10. A systemcomprising: one or more computers and one or more storage devicesstoring instructions that are operable, when executed by the one or morecomputers, to cause the one or more computers to perform operationscomprising: accessing transaction history data that, for each of one ormore past transactions, indicates past transaction details associatedwith the past transaction, a past predicted value, and a past finalvalue, wherein the past transaction details include a past sentimentassociated with a past requesting entity requesting the pasttransaction, a past value sensitivity of the past requesting entity, anda past relationship between the past requesting entity requesting thepast transaction and a past processing entity processing the pasttransaction; for each of the one or more past transactions, determininga past difference value between the past predicted value and the pastfinal value; training, using machine learning and the past transactiondetails and the past difference value, a predictive model that isconfigured to receive a given sentiment associated with a givenrequesting entity requesting a given transaction, a given valuesensitivity of the given requesting entity, a given relationship betweenthe given requesting entity and a given processing entity processing thegiven transaction, and a given predicted value associated with the giventransaction and output a given difference value to apply to the givenpredicted value; receiving a sentiment associated with a requestingentity requesting a transaction, a value sensitivity of the requestingentity, a relationship between the requesting entity and a processingentity processing the transaction, and a predicted value associated withthe transaction; providing the sentiment associated with the requestingentity requesting the transaction, the value sensitivity of therequesting entity, the relationship between the requesting entity andthe processing entity processing the transaction, and the predictedvalue associated with the transaction as input to the predictive model;in response to providing the sentiment associated with the requestingentity requesting the transaction, the value sensitivity of therequesting entity, the relationship between the requesting entity andthe processing entity processing the transaction, and the predictedvalue associated with the transaction as input to the predictive model,receiving, from the predictive model, an adjustment value to apply tothe predicted value; and providing, for output, data indicating theadjustment value.
 11. The system of claim 10, wherein: the pastsentiment associated with the past requesting entity requesting the pasttransaction is based on an analysis of past media that includes pastreferences to the past requesting entity requesting the past transactionand contexts of the past references, the given sentiment associated withthe given requesting entity requesting the given transaction is based onan analysis of given media that includes given references to the givenrequesting entity requesting the given transaction and contexts of thegiven references, and the sentiment associated with the requestingentity requesting the transaction is based on an analysis of media thatincludes references to the requesting entity requesting the transactionand contexts of the references.
 12. The system of claim 10, wherein: thepast value sensitivity of the past requesting entity requesting the pasttransaction is based on a past likelihood of the past requesting entityrequesting an adjustment to the past predicted value, the given valuesensitivity of the given requesting entity requesting the giventransaction is based on a given likelihood of the given requestingentity requesting an adjustment to the given predicted value, and thevalue sensitivity of the requesting entity requesting the transaction isbased on a likelihood of the requesting entity requesting an adjustmentto the predicted value.
 13. The system of claim 10, wherein: thetransaction is a stock trade, the predicted value is a predicted priceper share, and the adjustment value is an adjustment to the predictedprice per share.
 14. The system of claim 10, wherein: the pastrelationship between the past requesting entity requesting the pasttransaction and the past processing entity processing the pasttransaction is based on a past profitability realized by the pastprocessing entity processing the past transaction in providing pastservices to the past requesting entity requesting the past transaction,the given relationship between the given requesting entity requestingthe given transaction and the given processing entity processing thegiven transaction is based on a given profitability realized by thegiven processing entity processing the given transaction in providinggiven services to the given requesting entity requesting the giventransaction, and the relationship between the requesting entityrequesting the transaction and the processing entity processing thetransaction is based on a profitability realized by the processingentity processing the transaction in providing services to therequesting entity requesting the transaction.
 15. The system of claim10, wherein: the past relationship between the past requesting entityrequesting the past transaction and the past processing entityprocessing the past transaction is based on a number of changes the pastrequesting entity requesting the past transaction has requested for pastprevious transactions with the past processing entity, the givenrelationship between the given requesting entity requesting the giventransaction and the given processing entity processing the giventransaction is based on a number of changes the given requesting entityrequesting the given transaction has requested for given previoustransactions with the given processing entity, and the relationshipbetween the requesting entity requesting the transaction and theprocessing entity processing the transaction is based on a number ofchanges the requesting entity requesting the transaction has requestedfor previous transactions with the processing entity.
 16. Anon-transitory computer-readable medium storing software comprisinginstructions executable by one or more computers which, upon suchexecution, cause the one or more computers to perform operationscomprising: accessing transaction history data that, for each of one ormore past transactions, indicates past transaction details associatedwith the past transaction, a past predicted value, and a past finalvalue, wherein the past transaction details include a past sentimentassociated with a past requesting entity requesting the pasttransaction, a past value sensitivity of the past requesting entity, anda past relationship between the past requesting entity requesting thepast transaction and a past processing entity processing the pasttransaction; for each of the one or more past transactions, determininga past difference value between the past predicted value and the pastfinal value; training, using machine learning and the past transactiondetails and the past difference value, a predictive model that isconfigured to receive a given sentiment associated with a givenrequesting entity requesting a given transaction, a given valuesensitivity of the given requesting entity, a given relationship betweenthe given requesting entity and a given processing entity processing thegiven transaction, and a given predicted value associated with the giventransaction and output a given difference value to apply to the givenpredicted value; receiving a sentiment associated with a requestingentity requesting a transaction, a value sensitivity of the requestingentity, a relationship between the requesting entity and a processingentity processing the transaction, and a predicted value associated withthe transaction; providing the sentiment associated with the requestingentity requesting the transaction, the value sensitivity of therequesting entity, the relationship between the requesting entity andthe processing entity processing the transaction, and the predictedvalue associated with the transaction as input to the predictive model;in response to providing the sentiment associated with the requestingentity requesting the transaction, the value sensitivity of therequesting entity, the relationship between the requesting entity andthe processing entity processing the transaction, and the predictedvalue associated with the transaction as input to the predictive model,receiving, from the predictive model, an adjustment value to apply tothe predicted value; and providing, for output, data indicating theadjustment value.
 17. The medium of claim 16, wherein: the pastsentiment associated with the past requesting entity is a past real-timesentiment that is based on past real-time data associated with the pastrequesting entity, the given sentiment associated with the givenrequesting entity is a given real-time sentiment that is based on givenreal-time data associated with the given requesting entity, and thesentiment associated with the requesting entity is a real-time sentimentthat is based on real-time data associated with the requesting entity.18. The medium of claim 16, wherein: the past relationship between thepast requesting entity requesting the past transaction and the pastprocessing entity processing the past transaction is based on pastreal-time data associated with the past requesting entity and the pastprocessing entity, the given relationship between the given requestingentity requesting the given transaction and the given processing entityprocessing the given transaction is based on given real-time dataassociated with the given requesting entity and the given processingentity, and the relationship between the requesting entity requestingthe transaction and the processing entity processing the transaction isbased on real-time data associated with the requesting entity and theprocessing entity.
 19. The medium of claim 16, wherein the operationsfurther comprise: receiving data indicating an instruction to apply theadjustment value to the predicted value; and executing the transactionwith the predicted value adjusted by the adjustment value.
 20. Themedium of claim 16, wherein: the past relationship between the pastrequesting entity requesting the past transaction and the pastprocessing entity processing the past transaction is based on a numberof changes the past requesting entity requesting the past transactionhas requested for past previous transactions with the past processingentity, the given relationship between the given requesting entityrequesting the given transaction and the given processing entityprocessing the given transaction is based on a number of changes thegiven requesting entity requesting the given transaction has requestedfor given previous transactions with the given processing entity, andthe relationship between the requesting entity requesting thetransaction and the processing entity processing the transaction isbased on a number of changes the requesting entity requesting thetransaction has requested for previous transactions with the processingentity.